10.46243/jst.2021.v6.i04.pp377-382 registered
Sentiment Analysis using Machine Learning (The Sorting Hat)
Resolves to https://www.jst.org.in/index.php/pub/article/view/772
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2021.v6.i04.pp377-382
Registered 29 Sep 2026 via crossref · record version 2 · last change 30 Sep 2026, 12:00 AM · record sha256 9d4b1a2666842dc9…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
Sentiment Analysis using Machine Learning (The Sorting Hat) (PrincipalTitle)
Published 2021-08-16
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 06 · issue 01 · pages 377–382
Agents
- Prasad Wagh (author)
- Pratik Jaiswal (author)
- Ankit Rahangdale (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2021.v6.i04.pp377-382
Abstract
Sarcasm and Hate Speech is impacting societal harmony and peace. Considering the magnitude of this harmonious impact, there is a need to find a solution to curb the online spread of Hate Speech and Sarcasm. Detection of hate speech and sarcasm is being tackled with various approaches like manual checks, deep learning techniques in recent times, and statistical-based classification algorithms. These methods are unreliable due to the non-binary(true or false) nature of the tweets. Categorizing tweets requires deeper investigation such as classification on entirely positive or entirely negative rather than binary classification. In this paper - a snippet - The Sorting Hat, to detect sarcasm, hate-speech, and sentiments in the tweets using SVM (Support Vector Machine) and LSTM (Long short term memory) is proposed. The Sorting Hat classifies a given tweet into one of the six degrees of classification - “Positive”, “Negative”, “Neutral”, “Sarcasm”, “Non-sarcasm”, “Hate-speech”. The basic meaning of sarcasm which comes into our mind is a positive statement or sentiment attached to a negative situation or vice versa.The current system works on the outside whiсh has been assigned tо а раrtiсulаr tорiс. Current systems also do not determine the imрасt rating, the results are limited to whether they can be included in the раrtiсulаr processing field and do not allow retrieval of data based on user-generated query meaning that it has been selected.. Whereas the Sorting Hat will collect the tweets from the users manually. Collected tweets will be considered for further processing. We will then аррly the suрervised аlgоrithm оn the stоred dаtа. The supervised algorithm used in the оur system is Suрроrt Veсtоr Mасhine (SVM). The results of the algorithms i.e. emotions will be represented in a graphical way (bar charts). The proposed system works better compared to the existing one. This is because we will be able to obtain calculated figures from reрresentаtiоn оf result саn hаvе аny imрасt in the field of а раrtiсulаr. The overall product experience using The Sorting Hat largely intervenes the impulsive behavior of posting tweets, and thereby provides the solution to curb rampant spread of Hate Speech and better understanding of sarcastic tweets.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2021.v6.i04.pp377-382 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Sentiment Analysis using Machine Learning (The Sorting Hat) (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Prasad Wagh author: Pratik Jaiswal author: Ankit Rahangdale publisher: Longman Publishers published: 2021-08-16 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 377–382 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2026-09-11 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
complete Every System Metadata element is here, with the basic metadata a journal article needs.
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Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 76 fields set · sha256 28264ca7a481… |
| 2 | 30 Sep 2026, 12:00 AM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
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